WebTableX: Efficiently Discovering Web Table Transformations Through Sampling

Describing real-world entities varies widely across online sources, complicating the task of web data integration. We study the challenge of efficiently discovering syntactic transformations for web tables, where two tables are not initially equi-joinable but can become joinable after some transformations. Detecting these transformations is challenging due to the large space of possible candidates, which further grows with the number of rows. To overcome this, we introduce a cluster-based table sampling that reduces the number of required rows for transformation generation by capturing underlying data patterns. Our extensive evaluation demonstrates that the proposed method significantly improves the runtime performance of state-of-the-art methods on two real-world web datasets, sourced from webpage content and organizational forums, by one to two orders of magnitude with negligible impact on accuracy.